FDD3009 Computational Topics in Phylogeny Inference 6.0 credits

Beräkningsaspekter på fylogeni

Offering and execution

Course offering missing for current semester as well as for previous and coming semesters

Course information

Content and learning outcomes

Course contents *

Substitution models and their design.

Models for variation in evolutionary speed.

The tree reconsiliation and models of duplication and loss of genes.

Inference methods such as ML and MCMC.

Inference tools such as MrBayes and RevBayes.

Intended learning outcomes *

The general aim is to give basic knowledge of modelling of evolution.

After completed course, you should be able to

- be familiar with important evolutionary models of substitutions duplications/losses, intensity changes etc.

- apply these models on real data using popular tools

- understand inference methods that are used for phylogenetic problems

- adapt known models to new problems

Course Disposition

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Literature and preparations

Specific prerequisites *

No information inserted

Recommended prerequisites

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Examination and completion

If the course is discontinued, students may request to be examined during the following two academic years.

Grading scale *

P, F

Examination *

    Based on recommendation from KTH’s coordinator for disabilities, the examiner will decide how to adapt an examination for students with documented disability.

    The examiner may apply another examination format when re-examining individual students.

    Opportunity to complete the requirements via supplementary examination

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    Opportunity to raise an approved grade via renewed examination

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    Lars Arvestad

    Ethical approach *

    • All members of a group are responsible for the group's work.
    • In any assessment, every student shall honestly disclose any help received and sources used.
    • In an oral assessment, every student shall be able to present and answer questions about the entire assignment and solution.

    Further information

    Course web

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    Offered by

    EECS/Computational Science and Technology

    Main field of study *

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    Education cycle *

    Third cycle

    Add-on studies

    No information inserted


    Lars Arvestad

    Postgraduate course

    Postgraduate courses at EECS/Computational Science and Technology